Data Quality Management Solutions
Change is the only constant in today’s business environment. Whether you’re growing your business, entering new markets or undergoing a merger or acquisition, your business – and its data – is evolving and expanding.
That’s why data quality management has become a critical part of IT management. As you adopt new systems and retire old ones your ability to protect the quality and integrity of data is vital to business continuity.
Manage Data Quality Across the Enterprise…Easily
Liaison’s data quality management solutions meet the demand for scalable, high-performing technology infrastructure that is flexible enough to support data quality processes throughout the enterprise. We provide fast deployment of data quality management capabilities that deliver an immediate impact. Through our SOA-enabled platform, we give you direct access to query, validate, submit, and update data to ensure the highest level of quality.
Liaison’s data quality management solutions provide the following benefits:
- Standardize, cleanse, enrich and translate data: Liaison's data cleansing capabilities provide flexible solutions that enable you to analyze, improve and control corporate data. Your organization can automatically examine and correct transactional data and enforce business rules throughout the enterprise in real time.
- Comprehensive data cleansing and parsing capabilities: Our solutions help you to use business rules and reference data dictionaries to parse and standardize free-form text data elements.
- Robust data quality profiling capabilities: Liaison’s solutions give you the tools to use business rules and reference data to analyze and rank data according to completeness, conformity, consistency, duplication, integrity and accuracy.
- Open, content-based reference data dictionaries: Our data quality management solutions help you easily create, edit, and enhance reference data dictionaries at any time, as well as analyze and standardize content and implement data quality business rules using reference dictionaries.